I find myself getting asked about my chosen career path more and more frequently these days.1 To try and combat that, here is my best attempt to define what data science is. Maybe you saw my post on the tools you can use for analysis but regardless; if you’re interested in the field yet unclear what that entails, this should help clarify the demands of data science.
Why Now?
In short, because computers have gotten so much more powerful, we can now collect and process a ton of data.2 Unrelated, statistics has a ton of interesting techniques to answer questions like “does this affect that?” Or “can we predict this using these three measures?” There have been several breakthroughs recently that have increased the accuracy of these techniques.
Solving interesting problems
One day, a bright soul put these two ideas together. Merging these more powerful computers along with new and improved statistical techniques have led to businesses being able to use these resources in a practical manner. Data scientists can improve the bottom line of any business in a variety of ways.
As an example, say that I am in charge of a grocery store. I have data showing the products that were purchased together and the price that they were purchased at. I can use data science to say “what happens to the sales of Product B if I lower the price of Product A?” Pricing changes are critically important, yet were often based on gut instinct. Now we can test these conclusions and see if they’re valid.
Prediction and variance
In short, data science is using statistics and computers for prediction and analysis. Part statistician, part computer science, and part business analyst, data science is an exciting field to be entering in today’s day and age.
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It seems to be a hot topic among my grandparents, specifically. ↩︎
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The Economist among other places says that this will only be increasing as time progresses. ↩︎